Cobalt strike beacon https c2 heuristic detection

ABSTRACT

Techniques for Cobalt Strike Beacon HTTPS C2 heuristic detection are disclosed. In some embodiments, a system/process/computer program product for Cobalt Strike Beacon HTTPS C2 heuristic detection includes monitoring HyperText Transfer Protocol Secure (HTTPS) network traffic at a firewall; prefiltering the monitored HTTPS network traffic at the firewall to select a subset of the HTTPS network traffic to forward to a cloud security service; determining whether the subset of the HTTPS network traffic is associated with Cobalt Strike Beacon HTTPS C2 traffic activity based on a plurality of heuristics; and performing an action in response to detecting the Cobalt Strike Beacon HTTPS C2 traffic activity.

BACKGROUND OF THE INVENTION

Malware is a general term commonly used to refer to malicious software(e.g., including a variety of hostile, intrusive, and/or otherwiseunwanted software). Malware can be in the form of code, scripts, activecontent, and/or other software. Example uses of malware includedisrupting computer and/or network operations, stealing proprietaryinformation (e.g., confidential information, such as identity,financial, and/or intellectual property related information), and/orgaining access to private/proprietary computer systems and/or computernetworks. Unfortunately, as techniques are developed to help detect andmitigate malware, nefarious authors find ways to circumvent suchefforts. Accordingly, there is an ongoing need for improvements totechniques for identifying and mitigating malware.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 illustrates an example of an environment in which maliciousapplications (“malware”) are detected and prevented from causing harm.

FIG. 2A illustrates an embodiment of a data appliance.

FIG. 2B is a functional diagram of logical components of an embodimentof a data appliance.

FIG. 3 illustrates an example of logical components that can be includedin a system for analyzing samples.

FIG. 4A illustrates portions of an example embodiment of a detectionsystem and a quality check system for processing of network traffic forperforming Cobalt Strike Beacon C2 HTTP traffic detection in accordancewith some embodiments.

FIG. 4B illustrates portions of an example embodiment of a detectionsystem and a quality check system for processing of network traffic forperforming Cobalt Strike Beacon C2 HTTPS traffic detection in accordancewith some embodiments.

FIG. 5A illustrates example attributes associated with Cobalt StrikeBeacon HTTP traffic that is used for heuristic detection in accordancewith some embodiments.

FIG. 5B illustrates additional example attributes associated with CobaltStrike Beacon HTTP traffic that is used for heuristic detection inaccordance with some embodiments.

FIG. 5C illustrates additional example attributes associated with CobaltStrike Beacon HTTP traffic that is used for heuristic detection inaccordance with some embodiments.

FIG. 5D illustrates additional example attributes associated with CobaltStrike Beacon HTTP traffic that is used for heuristic detection inaccordance with some embodiments.

FIG. 5E illustrates additional example attributes associated with CobaltStrike Beacon HTTPS traffic that is used for heuristic detection inaccordance with some embodiments.

FIG. 5F illustrates additional example attributes associated with CobaltStrike Beacon HTTPS traffic that is used for heuristic detection inaccordance with some embodiments.

FIG. 5G illustrates additional example attributes associated with CobaltStrike Beacon HTTPS traffic that is used for heuristic detection inaccordance with some embodiments.

FIG. 6 is a flow diagram of a process for Cobalt Strike Beacon HTTP C2heuristic detection in accordance with some embodiments.

FIG. 7 is a flow diagram of a process for Cobalt Strike Beacon HTTPS C2heuristic detection in accordance with some embodiments.

FIG. 8 illustrates a checksum algorithm for probing logic for HTTP/HTTPSCobalt Strike TeamServer detection in accordance with some embodiments.

FIG. 9A illustrates an example DNS request for performing active probingof a target in accordance with some embodiments.

FIG. 9B illustrates an example DNS response to active probing of atarget in accordance with some embodiments.

FIG. 10 is a flow diagram of a process for HTTP/HTTPS probing for CobaltStrike TeamServer detection in accordance with some embodiments.

FIG. 11 is a flow diagram of a process for DNS probing for Cobalt StrikeTeamServer detection in accordance with some embodiments.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

A firewall generally protects networks from unauthorized access whilepermitting authorized communications to pass through the firewall. Afirewall is typically a device, a set of devices, or software executedon a device that provides a firewall function for network access. Forexample, a firewall can be integrated into operating systems of devices(e.g., computers, smart phones, or other types of network communicationcapable devices). A firewall can also be integrated into or executed asone or more software applications on various types of devices, such ascomputer servers, gateways, network/routing devices (e.g., networkrouters), and data appliances (e.g., security appliances or other typesof special purpose devices), and in various implementations, certainoperations can be implemented in special purpose hardware, such as anASIC or FPGA.

Firewalls typically deny or permit network transmission based on a setof rules. These sets of rules are often referred to as policies (e.g.,network policies or network security policies). For example, a firewallcan filter inbound traffic by applying a set of rules or policies toprevent unwanted outside traffic from reaching protected devices. Afirewall can also filter outbound traffic by applying a set of rules orpolicies (e.g., allow, block, monitor, notify or log, and/or otheractions can be specified in firewall rules or firewall policies, whichcan be triggered based on various criteria, such as are describedherein). A firewall can also filter local network (e.g., intranet)traffic by similarly applying a set of rules or policies.

Security devices (e.g., security appliances, security gateways, securityservices, and/or other security devices) can include various securityfunctions (e.g., firewall, anti-malware, intrusion prevention/detection,Data Loss Prevention (DLP), and/or other security functions), networkingfunctions (e.g., routing, Quality of Service (QoS), workload balancingof network related resources, and/or other networking functions), and/orother functions. For example, routing functions can be based on sourceinformation (e.g., IP address and port), destination information (e.g.,IP address and port), and protocol information.

A basic packet filtering firewall filters network communication trafficby inspecting individual packets transmitted over a network (e.g.,packet filtering firewalls or first generation firewalls, which arestateless packet filtering firewalls). Stateless packet filteringfirewalls typically inspect the individual packets themselves and applyrules based on the inspected packets (e.g., using a combination of apacket's source and destination address information, protocolinformation, and a port number).

Application firewalls can also perform application layer filtering(e.g., application layer filtering firewalls or second generationfirewalls, which work on the application level of the TCP/IP stack).Application layer filtering firewalls or application firewalls cangenerally identify certain applications and protocols (e.g., webbrowsing using HyperText Transfer Protocol (HTTP), a Domain Name System(DNS) request, a file transfer using File Transfer Protocol (FTP), andvarious other types of applications and other protocols, such as Telnet,DHCP, TCP, UDP, and TFTP (GSS)). For example, application firewalls canblock unauthorized protocols that attempt to communicate over a standardport (e.g., an unauthorized/out of policy protocol attempting to sneakthrough by using a non-standard port for that protocol can generally beidentified using application firewalls).

Stateful firewalls can also perform state-based packet inspection inwhich each packet is examined within the context of a series of packetsassociated with that network transmission's flow of packets. Thisfirewall technique is generally referred to as a stateful packetinspection as it maintains records of all connections passing throughthe firewall and is able to determine whether a packet is the start of anew connection, a part of an existing connection, or is an invalidpacket. For example, the state of a connection can itself be one of thecriteria that triggers a rule within a policy.

Advanced or next generation firewalls can perform stateless and statefulpacket filtering and application layer filtering as discussed above.Next generation firewalls can also perform additional firewalltechniques. For example, certain newer firewalls sometimes referred toas advanced or next generation firewalls can also identify users andcontent (e.g., next generation firewalls). In particular, certain nextgeneration firewalls are expanding the list of applications that thesefirewalls can automatically identify to thousands of applications.Examples of such next generation firewalls are commercially availablefrom Palo Alto Networks, Inc. (e.g., Palo Alto Networks' PA Seriesfirewalls). For example, Palo Alto Networks' next generation firewallsenable enterprises to identify and control applications, users, andcontent—not just ports, IP addresses, and packets—using variousidentification technologies, such as the following: APP-ID for accurateapplication identification, User-ID for user identification (e.g., byuser or user group), and Content-ID for real-time content scanning(e.g., controlling web surfing and limiting data and file transfers).These identification technologies allow enterprises to securely enableapplication usage using business-relevant concepts, instead of followingthe traditional approach offered by traditional port-blocking firewalls.Also, special purpose hardware for next generation firewalls(implemented, for example, as dedicated appliances) generally providehigher performance levels for application inspection than softwareexecuted on general purpose hardware (e.g., such as security appliancesprovided by Palo Alto Networks, Inc., which use dedicated, functionspecific processing that is tightly integrated with a single-passsoftware engine to maximize network throughput while minimizinglatency).

Advanced or next generation firewalls can also be implemented usingvirtualized firewalls. Examples of such next generation firewalls arecommercially available from Palo Alto Networks, Inc. (e.g., Palo AltoNetworks' VM Series firewalls, which support various commercialvirtualized environments, including, for example, VMware® ESXi™ andNSX™, Citrix® Netscaler SDX™, KVM/OpenStack (Centos/RHEL, Ubuntu®), andAmazon Web Services (AWS)) as well as CN Series container nextgeneration firewalls. For example, virtualized firewalls can supportsimilar or the exact same next-generation firewall and advanced threatprevention features available in physical form factor appliances,allowing enterprises to safely enable applications flowing into, andacross their private, public, and hybrid cloud computing environments.Automation features such as VM monitoring, dynamic address groups, and aREST-based API allow enterprises to proactively monitor VM changesdynamically feeding that context into security policies, therebyeliminating the policy lag that may occur when VMs change.

Overview of Techniques for Cobalt Strike Beacon HTTP/HTTPS C2 HeuristicDetection

Generally, existing anti-malware security solutions often fail to detectnew malware or new malware variants based on malware signatures (e.g.,predefined patterns, such as Intrusion Prevention System (IPS)signatures). Specifically, existing anti-malware security solutionsgenerally fail to detect new malware or new malware variants if amalware signature for that new malware or new malware variant does notyet exist (e.g., currently, signature-based content IPS solutionsgenerally cannot detect the Cobalt Strike Beacon C2 traffic effectivelyas only the default profile(s) or known profile(s) can typically bedetected using existing signature-based content IPS solutions). Theseshortcomings associated with existing malware solutions exposeenterprises to significant security risks due to the failure to detectsuch new malware or new malware variants.

Cobalt Strike is an example of a type of malware using evasiontechniques to bypass malware solutions dependent on pattern matchingbased on preexisting malware signatures (e.g., penetration testingservice providers (pen testers) often use Cobalt Strike (CS) tools totest commercially available security solutions, such as firewallsecurity solutions). Cobalt Strike is a commercially/publicly availabletoolkit that is often used by researchers and penetration testers.However, it can also be used by attackers/hackers to infiltrateenterprise networks for unauthorized/nefarious purposes (e.g.,exfiltration of confidential/proprietary data associated with enterprisenetworks, etc.).

Specifically, malware writers can use a self-defined command and control(C2 or C&C) profile configuration for Cobalt Strike to avoid malwaresolutions dependent on pattern matching based on preexisting malwaresignatures. The Cobalt Strike toolkit generates C2 traffic that can bebased on various protocols, including HyperText Transfer Protocol(HTTP), HyperText Transfer Protocol Secure (HTTPS), and Domain NameSystem (DNS) protocols.

Thus, what are needed are anti-malware security solutions that canefficiently and effectively detect Cobalt Strike Beacon C2 HTTP/HTTPStraffic.

Accordingly, new and improved techniques for Cobalt Strike BeaconHTTP/HTTPS C2 heuristic detection are disclosed.

A new malware detection solution is disclosed that includes a newbehavior-based detection solution (e.g., using heuristic-basedtechniques) to efficiently and effectively detect Cobalt Strike beaconcommand-and-control (C2 or C&C) traffic (e.g., a type of maliciousnetwork communication between a C2 server and malware on an infectedhost) using HyperText Transfer Protocol (HTTP) or HyperText TransferProtocol Secure (HTTPS) protocols. The new malware detection solutioncan determine a verdict of the sample as malware using heuristics tofacilitate detection of Cobalt Strike Beacon C2 HTTP/HTTPS traffic basedon the monitored network traffic activity even if there is not anexisting IPS signature that would effectively detect the Cobalt StrikeBeacon C2 HTTP/HTTPS traffic.

Specifically, a new behavior-based detection solution for detectingCobalt Strike Beacon C2 HTTP/HTTPS traffic is disclosed that includes adetection system (e.g., including an Intrusion Prevention System (IPS))and a quality check system. The detection system can facilitatedetection of Cobalt Strike Beacon C2 HTTP/HTTPS traffic usingheuristic-based techniques, such as will be further described below withrespect to various embodiments.

In some embodiments, a system/process/computer program product forCobalt Strike Beacon HTTP/HTTPS C2 heuristic detection includesmonitoring HTTP/HTTPS network traffic at a firewall; prefiltering themonitored HTTP/HTTPS network traffic at the firewall to select a subsetof the HTTPS network traffic to forward to a cloud security service;determining whether the subset of the HTTP/HTTPS network traffic isassociated with Cobalt Strike Beacon HTTP/HTTPS C2 traffic activitybased on a plurality of heuristics; and performing an action in responseto detecting the Cobalt Strike Beacon HTTP/HTTPS C2 traffic activity.

In some embodiments, a system/process/computer program product forCobalt Strike Beacon HTTP/HTTPS C2 heuristic detection further includesusing a fast match table of a detection system for storing previouslydetected Cobalt Strike Beacon HTTP/HTTPS C2 traffic activity, whereinthe fast match table of the detection system stores a 3-tuple ofpreviously detected Cobalt Strike Beacon HTTP/HTTPS C2 traffic activity,and wherein the 3-tuple includes a source IP address, a destination IPaddress, and a destination port.

In some embodiments, a system/process/computer program product forCobalt Strike Beacon HTTP/HTTPS C2 heuristic detection further includesstoring data statistics based on an automated heuristic analysis of thesubset of the HTTP/HTTPS network traffic, wherein the data statisticsare stored in a data statistics table of a detection system.

In some embodiments, a system/process/computer program product forCobalt Strike Beacon HTTP/HTTPS C2 heuristic detection further includesperforming a validation of the detected Cobalt Strike Beacon HTTP/HTTPSC2 traffic activity based on probing of a destination IP addressassociated with the detected Cobalt Strike Beacon HTTP/HTTPS C2 trafficactivity and using a fingerprint data store.

Accordingly, new and improved security solutions that facilitate CobaltStrike Beacon HTTP/HTTPS C2 heuristic detection using a securityplatform (e.g., a firewall (FW)/Next Generation Firewall (NGFW), anetwork sensor acting on behalf of the firewall, or another (virtual)device/component that can implement security policies using thedisclosed techniques, including, for example, Palo Alto Networks' PASeries next generation firewalls, Palo Alto Networks' VM Seriesvirtualized next generation firewalls, and CN Series container nextgeneration firewalls, and/or other commercially available virtual-basedor container-based firewalls can similarly be implemented and configuredto perform the disclosed techniques) are disclosed in accordance withsome embodiments.

These and other embodiments and examples for Cobalt Strike BeaconHTTP/HTTPS C2 heuristic detection will be further described below.

Example System Architectures for Cobalt Strike Beacon HTTP/HTTPS C2Heuristic Detection

Accordingly, in some embodiments, the disclosed techniques includeproviding a security platform (e.g., the securityfunction(s)/platform(s) can be implemented using a firewall (FW)/NextGeneration Firewall (NGFW), a network sensor acting on behalf of thefirewall, or another (virtual) device/component that can implementsecurity policies using the disclosed techniques, such as PANOSexecuting on a virtual/physical NGFW solution commercially availablefrom Palo Alto Networks, Inc. or another security platform/NFGW,including, for example, Palo Alto Networks' PA Series next generationfirewalls, Palo Alto Networks' VM Series virtualized next generationfirewalls, and CN Series container next generation firewalls, and/orother commercially available virtual-based or container-based firewallscan similarly be implemented and configured to perform the disclosedtechniques) configured to provide DPI capabilities (e.g., includingstateful inspection) of, for example, applying the disclosed techniquesfor automatically detecting Cobalt Strike beacon C2 HTTP/HTTPS traffic,as further described below.

FIG. 1 illustrates an example of an environment in which maliciousapplications (“malware”) are detected and prevented from causing harm.As will be described in more detail below, malware classifications(e.g., as made by security platform 122) can be variously shared and/orrefined among various entities included in the environment shown in FIG.1 . And, using techniques described herein, devices, such as endpointclient devices 104-110, can be protected from such malware (e.g.,including previously unknown/new variants of malware, such as C2malware).

“Malware” as used herein refers to an application that engages inbehaviors, whether clandestinely or not (and whether illegal or not), ofwhich a user does not approve/would not approve if fully informed.Examples of malware include ransomware, Trojans, viruses, rootkits,spyware, hacking tools, etc. One example of malware is a desktop/mobileapplication that encrypts a user's stored data (e.g., ransomware).Another example of malware is C2 malware, such as similarly describedabove. Other forms of malware (e.g., keyloggers) can also bedetected/thwarted using the disclosed techniques for sample trafficbased self-learning malware detection as will be further describedherein.

Techniques described herein can be used in conjunction with a variety ofplatforms (e.g., servers, computing appliances, virtual/containerenvironments, desktops, mobile devices, gaming platforms, embeddedsystems, etc.) and/or for automated detection of a variety of forms ofmalware (e.g., new and/or variants of malware, such as C2 malware,etc.). In the example environment shown in FIG. 1 , client devices104-108 are a laptop computer, a desktop computer, and a tablet(respectively) present in an enterprise network 140. Client device 110is a laptop computer present outside of enterprise network 140.

Data appliance 102 is configured to enforce policies regardingcommunications between client devices, such as client devices 104 and106, and nodes outside of enterprise network 140 (e.g., reachable viaexternal network 118). Examples of such policies include ones governingtraffic shaping, quality of service, and routing of traffic. Otherexamples of policies include security policies such as ones requiringthe scanning for threats in incoming (and/or outgoing) emailattachments, website content, files exchanged through instant messagingprograms, and/or other file transfers. In some embodiments, dataappliance 102 is also configured to enforce policies with respect totraffic that stays within enterprise network 140.

An embodiment of a data appliance is shown in FIG. 2A. The example shownis a representation of physical components that are included in dataappliance 102, in various embodiments. Specifically, data appliance 102includes a high performance multi-core Central Processing Unit (CPU) 202and Random Access Memory (RAM) 204. Data appliance 102 also includes astorage 210 (such as one or more hard disks or solid state storageunits). In various embodiments, data appliance 102 stores (whether inRAM 204, storage 210, and/or other appropriate locations) informationused in monitoring enterprise network 140 and implementing disclosedtechniques. Examples of such information include applicationidentifiers, content identifiers, user identifiers, requested URLs, IPaddress mappings, policy and other configuration information,signatures, hostname/URL categorization information, malware profiles,and machine learning (ML) models (e.g., such as for sample traffic basedself-learning malware detection, include C2 ML models, as furtherdescribed herein). Data appliance 102 can also include one or moreoptional hardware accelerators. For example, data appliance 102 caninclude a cryptographic engine 206 configured to perform encryption anddecryption operations, and one or more Field Programmable Gate Arrays(FPGAs) 208 configured to perform matching, act as network processors,and/or perform other tasks.

Functionality described herein as being performed by data appliance 102can be provided/implemented in a variety of ways. For example, dataappliance 102 can be a dedicated device or set of devices. Thefunctionality provided by data appliance 102 can also be integrated intoor executed as software on a general purpose computer, a computerserver, a gateway, and/or a network/routing device. In some embodiments,at least some services described as being provided by data appliance 102are instead (or in addition) provided to a client device (e.g., clientdevice 104 or client device 110) by software executing on the clientdevice.

Whenever data appliance 102 is described as performing a task, a singlecomponent, a subset of components, or all components of data appliance102 may cooperate to perform the task. Similarly, whenever a componentof data appliance 102 is described as performing a task, a subcomponentmay perform the task and/or the component may perform the task inconjunction with other components. In various embodiments, portions ofdata appliance 102 are provided by one or more third parties. Dependingon factors such as the amount of computing resources available to dataappliance 102, various logical components and/or features of dataappliance 102 may be omitted and the techniques described herein adaptedaccordingly. Similarly, additional logical components/features can beincluded in embodiments of data appliance 102 as applicable. One exampleof a component included in data appliance 102 in various embodiments isan application identification engine which is configured to identify anapplication (e.g., using various application signatures for identifyingapplications based on packet flow analysis). For example, theapplication identification engine can determine what type of traffic asession involves, such as Web Browsing—Social Networking; WebBrowsing—News; SSH; and so on.

FIG. 2B is a functional diagram of logical components of an embodimentof a data appliance. The example shown is a representation of logicalcomponents that can be included in data appliance 102 in variousembodiments. Unless otherwise specified, various logical components ofdata appliance 102 are generally implementable in a variety of ways,including as a set of one or more scripts (e.g., written in Java,python, etc., as applicable).

As shown, data appliance 102 comprises a firewall, and includes amanagement plane 232 and a data plane 234. The management plane isresponsible for managing user interactions, such as by providing a userinterface for configuring policies and viewing log data. The data planeis responsible for managing data, such as by performing packetprocessing and session handling.

Network processor 236 is configured to receive packets from clientdevices, such as client device 108, and provide them to data plane 234for processing. Whenever flow module 238 identifies packets as beingpart of a new session, it creates a new session flow. Subsequent packetswill be identified as belonging to the session based on a flow lookup.If applicable, SSL decryption is applied by SSL decryption engine 240.Otherwise, processing by SSL decryption engine 240 is omitted.Decryption engine 240 can help data appliance 102 inspect and controlSSL/TLS and SSH encrypted traffic, and thus help to stop threats thatmight otherwise remain hidden in encrypted traffic. Decryption engine240 can also help prevent sensitive content from leaving enterprisenetwork 140. Decryption can be controlled (e.g., enabled or disabled)selectively based on parameters such as: URL category, traffic source,traffic destination, user, user group, and port. In addition todecryption policies (e.g., that specify which sessions to decrypt),decryption profiles can be assigned to control various options forsessions controlled by the policy. For example, the use of specificcipher suites and encryption protocol versions can be required.

Application identification (APP-ID) engine 242 is configured todetermine what type of traffic a session involves. As one example,application identification engine 242 can recognize a GET request inreceived data and conclude that the session requires an HTTP decoder. Insome cases, such as a web browsing session, the identified applicationcan change, and such changes will be noted by data appliance 102. Forexample, a user may initially browse to a corporate Wiki (classifiedbased on the URL visited as “Web Browsing—Productivity”) and thensubsequently browse to a social networking site (classified based on theURL visited as “Web Browsing—Social Networking”). Distinct types ofprotocols have corresponding decoders.

Based on the determination made by application identification engine242, the packets are sent, by threat engine 244, to an appropriatedecoder configured to assemble packets (which may be received out oforder) into the correct order, perform tokenization, and extract outinformation. Threat engine 244 also performs signature matching todetermine what should happen to the packet. As needed, SSL encryptionengine 246 can re-encrypt decrypted data. Packets are forwarded using aforward module 248 for transmission (e.g., to a destination).

As also shown in FIG. 2B, policies 252 are received and stored inmanagement plane 232. Policies can include one or more rules, which canbe specified using domain and/or host/server names, and rules can applyone or more signatures or other matching criteria or heuristics, such asfor security policy enforcement for subscriber/IP flows based on variousextracted parameters/information from monitored session traffic flows.Example policies can include C2 malware detection policies using thedisclosed techniques for sample traffic based self-learning malwaredetection. An interface (I/F) communicator 250 is provided formanagement communications (e.g., via (REST) APIs, messages, or networkprotocol communications or other communication mechanisms).

Security Platform

Returning to FIG. 1 , suppose a malicious individual (using system 120)has created malware 130, such as malware for generating Cobalt Strikebeacon C2 HTTP/HTTPS traffic using a new/variant profile to avoiddetection by preexisting IPS signatures (e.g., the malware can bedelivered to endpoint devices of users via a compromised web site whenthe user visits/browses to the compromised web site or via a phishingattack, etc.). The malicious individual hopes that a client device, suchas client device 104, will execute a copy of malware 130 to unpack themalware executable/payload, compromising the client device, and, e.g.,causing the client device to become a bot in a botnet. The compromisedclient device can then be instructed to perform tasks (e.g.,cryptocurrency mining, or participating in denial of service attacks)and to report information to an external entity, such as command andcontrol (C2/C&C) server 150, as well as to receive instructions from C2server 150, as applicable.

Suppose data appliance 102 has intercepted an email sent (e.g., bysystem 120) to a user, “Alice,” who operates client device 104. In thisexample, Alice receives the email and clicks on the link to aphishing/compromised site that could result in an attempted download ofmalware 130 by Alice's client device 104. However, in this example, dataappliance 102 can perform the disclosed techniques for sample trafficbased self-learning malware detection and block access from Alice'sclient device 104 to the packed malware content and to thereby preemptand prevent any such download of malware 130 to Alice's client device104. As will be further described below, data appliance 102 performs thedisclosed techniques for sample traffic based self-learning malwaredetection, such as further described below, to detect and block suchmalware 130 from harming Alice's client device 104.

In various embodiments, data appliance 102 is configured to work incooperation with security platform 122. As one example, securityplatform 122 can provide to data appliance 102 a set of signatures ofknown-malicious files (e.g., as part of a subscription). If a signaturefor malware 130 is included in the set (e.g., an MD hash of malware130), data appliance 102 can prevent the transmission of malware 130 toclient device 104 accordingly (e.g., by detecting that an MD5 hash ofthe email attachment sent to client device 104 matches the MD hash ofmalware 130). Security platform 122 can also provide to data appliance102 a list of known malicious domains and/or IP addresses, allowing dataappliance 102 to block traffic between enterprise network 140 and C2server 150 (e.g., where C&C server 150 is known to be malicious). Thelist of malicious domains (and/or IP addresses) can also help dataappliance 102 determine when one of its nodes has been compromised. Forexample, if client device 104 attempts to contact C2 server 150, suchattempt is a strong indicator that client 104 has been compromised bymalware (and remedial actions should be taken accordingly, such asquarantining client device 104 from communicating with other nodeswithin enterprise network 140).

As will be described in more detail below, security platform 122 canalso receive a copy of malware 130 from data appliance 102 to performcloud-based security analysis for performing sample traffic basedself-learning malware detection, and the malware verdict can be sentback to data appliance 102 for enforcing the security policy to therebysafeguard Alice's client device 104 from execution of malware 130 (e.g.,to block malware 130 from access on client device 104).

Further, security platform 122 can also provide other types ofinformation to data appliance 102 (e.g., as part of a subscription) suchas a set of information for performing the disclosed techniques forsample traffic based self-learning malware detection usable by dataappliance 102 to perform inline analysis of such malware files as willbe further described below.

A variety of actions can be taken by data appliance 102 if no signaturefor an attachment is found, in various embodiments. As a first example,data appliance 102 can fail-safe, by blocking transmission of anyattachments not whitelisted as benign (e.g., not matching signatures ofknown good files). A drawback of this approach is that there may be manylegitimate attachments unnecessarily blocked as potential malware whenthey are in fact benign. As a second example, data appliance 102 canfail-danger, by allowing transmission of any attachments not blacklistedas malicious (e.g., not matching signatures of known bad files). Adrawback of this approach is that newly created malware (previouslyunseen by platform 122) will not be prevented from causing harm. As athird example, data appliance 102 can be configured to provide the file(e.g., malware 130) to security platform 122 for static/dynamicanalysis, to determine whether it is malicious and/or to otherwiseclassify it.

Security platform 122 stores copies of received samples in storage 142and analysis is commenced (or scheduled, as applicable). One example ofstorage 142 is an Apache Hadoop Cluster (HDFS). Results of analysis (andadditional information pertaining to the applications) are stored indatabase 146. In the event an application is determined to be malicious,data appliances can be configured to automatically block the filedownload based on the analysis result. Further, a signature can begenerated for the malware and distributed (e.g., to data appliances suchas data appliances 102, 136, and 148) to automatically block future filetransfer requests to download the file determined to be malicious.

In various embodiments, security platform 122 comprises one or morededicated commercially available hardware servers (e.g., havingmulti-core processor(s), 32G+ of RAM, gigabit network interfaceadaptor(s), and hard drive(s)) running typical server-class operatingsystems (e.g., Linux). Security platform 122 can be implemented across ascalable infrastructure comprising multiple such servers, solid statedrives, and/or other applicable high-performance hardware. Securityplatform 122 can comprise several distributed components, includingcomponents provided by one or more third parties. For example, portionsor all of security platform 122 can be implemented using the AmazonElastic Compute Cloud (EC2) and/or Amazon Simple Storage Service (S3).Further, as with data appliance 102, whenever security platform 122 isreferred to as performing a task, such as storing data or processingdata, it is to be understood that a sub-component or multiplesub-components of security platform 122 (whether individually or incooperation with third party components) may cooperate to perform thattask. As one example, security platform 122 can optionally performstatic/dynamic analysis in cooperation with one or more virtual machine(VM) servers, such as VM server 124.

An example of a virtual machine server is a physical machine comprisingcommercially available server-class hardware (e.g., a multi-coreprocessor, 32+ Gigabytes of RAM, and one or more Gigabit networkinterface adapters) that runs commercially available virtualizationsoftware, such as VMware ESXi, Citrix XenServer, or Microsoft Hyper-V.In some embodiments, the virtual machine server is omitted. Further, avirtual machine server may be under the control of the same entity thatadministers security platform 122, but may also be provided by a thirdparty. As one example, the virtual machine server can rely on EC2, withthe remainder portions of security platform 122 provided by dedicatedhardware owned by and under the control of the operator of securityplatform 122. VM server 124 is configured to provide one or more virtualmachines 126-128 for emulating client devices. The virtual machines canexecute a variety of operating systems and/or versions thereof. Observedbehaviors resulting from executing applications in the virtual machinesare logged and analyzed (e.g., for indications that the application ismalicious). In some embodiments, log analysis is performed by the VMserver (e.g., VM server 124). In other embodiments, analysis isperformed at least in part by other components of security platform 122,such as a coordinator 144.

In various embodiments, security platform 122 makes available results ofits analysis of samples via a list of signatures (and/or otheridentifiers) to data appliance 102 as part of a subscription. Forexample, security platform 122 can periodically send a content packagethat identifies malware files, including for network traffic basedheuristic IPS malware detection, etc. (e.g., daily, hourly, or someother interval, and/or based on an event configured by one or morepolicies). An example content package includes a Cobalt Strike Beacon(CSB) detector 154 and/or other information (e.g., ML-based detectionmodels), such as further described below. The subscription can cover theanalysis of just those files intercepted by data appliance 102 and sentto security platform 122 by data appliance 102, and can also coversignatures of malware known to security platform 122. As will bedescribed in more detail below, platform 122 can also utilize othertypes of information/IL models for performing network traffic basedheuristic IPS malware detection. Specifically, platform 122 can utilizeCSB detector 154 (e.g., C2 ML model(s) that can be implemented as aplugin or sub-component of platform 122, such as will be furtherdescribed below, such as with respect to FIGS. 4A and 4B), which canhelp data appliance 102 detect and perform inline blocking ofpotentially new/variant C2 malware (e.g., Cobalt Strike beacon C2HTTP/HTTPS traffic).

In various embodiments, security platform 122 is configured to providesecurity services to a variety of entities in addition to (or, asapplicable, instead of) an operator of data appliance 102. For example,other enterprises, having their own respective enterprise networks 114and 116, and their own respective data appliances 136 and 148, cancontract with the operator of security platform 122. Other types ofentities can also make use of the services of security platform 122. Forexample, an Internet Service Provider (ISP) providing Internet serviceto client device 110 can contract with security platform 122 to analyzeapplications which client device 110 attempts to download. As anotherexample, the owner of client device 110 can install software on clientdevice 110 that communicates with security platform 122 (e.g., toreceive content packages from security platform 122, use the receivedcontent packages to check attachments in accordance with techniquesdescribed herein, and transmit applications to security platform 122 foranalysis).

Analyzing Samples Using Static/Dynamic Analysis

FIG. 3 illustrates an example of logical components that can be includedin a system for analyzing samples. Analysis system 300 can beimplemented using a single device. For example, the functionality ofanalysis system 300 can be implemented in a malware analysis module 112incorporated into data appliance 102. Analysis system 300 can also beimplemented, collectively, across multiple distinct devices. Forexample, the functionality of analysis system 300 can be provided bysecurity platform 122.

In various embodiments, analysis system 300 makes use of lists,databases, or other collections of known safe content and/or known badcontent (collectively shown in FIG. 3 as collection 314). Collection 314can be obtained in a variety of ways, including via a subscriptionservice (e.g., provided by a third party) and/or as a result of otherprocessing (e.g., performed by data appliance 102 and/or securityplatform 122). Examples of information included in collection 314 are:URLs, domain names, and/or IP addresses of known malicious servers;URLs, domain names, and/or IP addresses of known safe servers; URLs,domain names, and/or IP addresses of known command and control (C2/C&C)domains; signatures, hashes, and/or other identifiers of known maliciousapplications; signatures, hashes, and/or other identifiers of known safeapplications; signatures, hashes, and/or other identifiers of knownmalicious files (e.g., OS exploit files); signatures, hashes, and/orother identifiers of known safe libraries; and signatures, hashes,and/or other identifiers of known malicious libraries.

In various embodiments, when a new sample is received for analysis(e.g., an existing signature associated with the sample is not presentin analysis system 300), it is added to queue 302. As shown in FIG. 3 ,application 130 is received by system 300 and added to queue 302.

Coordinator 304 monitors queue 302, and as resources (e.g., a staticanalysis worker) become available, coordinator 304 fetches a sample fromqueue 302 for processing (e.g., fetches a copy of malware 130). Inparticular, coordinator 304 first provides the sample to static analysisengine 306 for static analysis. In some embodiments, one or more staticanalysis engines are included within analysis system 300, where analysissystem 300 is a single device. In other embodiments, static analysis isperformed by a separate static analysis server that includes a pluralityof workers (i.e., a plurality of instances of static analysis engine306).

The static analysis engine obtains general information about the sample,and includes it (along with heuristic and other information, asapplicable) in a static analysis report 308. The report can be createdby the static analysis engine, or by coordinator 304 (or by anotherappropriate component) which can be configured to receive theinformation from static analysis engine 306. As an example, staticanalysis of malware can include performing a signature-based analysis.In some embodiments, the collected information is stored in a databaserecord for the sample (e.g., in database 316), instead of or in additionto a separate static analysis report 308 being created (i.e., portionsof the database record form the report 308). In some embodiments, thestatic analysis engine also forms a verdict with respect to theapplication (e.g., “safe,” “suspicious,” or “malicious”). As oneexample, the verdict can be “malicious” if even one “malicious” staticfeature is present in the application (e.g., the application includes ahard link to a known malicious domain). As another example, points canbe assigned to each of the features (e.g., based on severity if found;based on how reliable the feature is for predicting malice; etc.) and averdict can be assigned by static analysis engine 306 (or coordinator304, if applicable) based on the number of points associated with thestatic analysis results.

Once static analysis is completed, coordinator 304 locates an availabledynamic analysis engine 310 to perform dynamic analysis on theapplication. As with static analysis engine 306, analysis system 300 caninclude one or more dynamic analysis engines directly. In otherembodiments, dynamic analysis is performed by a separate dynamicanalysis server that includes a plurality of workers (i.e., a pluralityof instances of dynamic analysis engine 310).

Each dynamic analysis worker manages a virtual machine instance (e.g.,emulation/sandbox analysis of samples for malware detection, such as theabove-described C2 malware detection based on monitored network trafficactivity). In some embodiments, results of static analysis (e.g.,performed by static analysis engine 306), whether in report form (308)and/or as stored in database 316, or otherwise stored, are provided asinput to dynamic analysis engine 310. For example, the static reportinformation can be used to help select/customize the virtual machineinstance used by dynamic analysis engine 310 (e.g., Microsoft Windows 7SP 2 vs. Microsoft Windows 10 Enterprise, or iOS 11.0 vs. iOS 12.0).Where multiple virtual machine instances are executed at the same time,a single dynamic analysis engine can manage all of the instances, ormultiple dynamic analysis engines can be used (e.g., with each managingits own virtual machine instance), as applicable. As will be explainedin more detail below, during the dynamic portion of the analysis,actions taken by the application (including network activity) areanalyzed.

In various embodiments, static analysis of a sample is omitted or isperformed by a separate entity, as applicable. As one example,traditional static and/or dynamic analysis may be performed on files bya first entity. Once it is determined (e.g., by the first entity) that agiven file is malicious, the file can be provided to a second entity(e.g., the operator of security platform 122) specifically foradditional analysis with respect to the malware's use of networkactivity (e.g., by a dynamic analysis engine 310).

The environment used by analysis system 300 is instrumented/hooked suchthat behaviors observed while the application is executing are logged asthey occur (e.g., using a customized kernel that supports hooking andlogcat). Network traffic associated with the emulator is also captured(e.g., using pcap). The log/network data can be stored as a temporaryfile on analysis system 300, and can also be stored more permanently(e.g., using HDFS or another appropriate storage technology orcombinations of technology, such as MongoDB). The dynamic analysisengine (or another appropriate component) can compare the connectionsmade by the sample to lists of domains, IP addresses, etc. (314) anddetermine whether the sample has communicated (or attempted tocommunicate) with malicious entities.

As with the static analysis engine, the dynamic analysis engine storesthe results of its analysis in database 316 in the record associatedwith the application being tested (and/or includes the results in report312 as applicable). In some embodiments, the dynamic analysis enginealso forms a verdict with respect to the application (e.g., “safe,”“suspicious,” or “malicious”). As one example, the verdict can be“malicious” if even one “malicious” action is taken by the application(e.g., an attempt to contact a known malicious domain is made, or anattempt to exfiltrate sensitive information is observed). As anotherexample, points can be assigned to actions taken (e.g., based onseverity if found; based on how reliable the action is for predictingmalice; etc.) and a verdict can be assigned by dynamic analysis engine310 (or coordinator 304, if applicable) based on the number of pointsassociated with the dynamic analysis results. In some embodiments, afinal verdict associated with the sample is made based on a combinationof report 308 and report 312 (e.g., by coordinator 304).

Cobalt Strike Beacon HTTP C2 Heuristic Detection

FIG. 4A illustrates portions of an example embodiment of a detectionsystem and a quality check system for processing of network traffic forperforming Cobalt Strike Beacon C2 HTTP traffic detection in accordancewith some embodiments. As similarly discussed above, in variousembodiments, security platform 122 includes a Cobalt Strike Beacon (CSB)detector 154. FIG. 4A illustrates subcomponents of CSB detector 154including the following sub-components: a detection system 402 and aquality check system 404.

Referring to FIG. 4A, a behavior-based and cross-session detectionsolution for performing Cobalt Strike beacon C2 HTTP traffic detectionis performed using a cloud-based security service (e.g., cloud securityservice 122 as shown in FIG. 1 ) in coordination with a data appliance(e.g., a data appliance implementing a firewall, also referred to belowsimply as a firewall) 102. The behavior-based detection solutionperforms a cross-session check and includes three components as will bedescribed below.

Firewall 102 monitors network traffic on an enterprise network (e.g.,including HTTP traffic on enterprise network 140, such as shown in FIG.1 ). As shown at 410, firewall 102 monitors network traffic using a dataplane and performs a prefiltering analysis of the network traffic usingan HTTP prefilter module (e.g., sub-component). As shown at 412,firewall 102 forwards the traffic (e.g., a packet capture (pcap) filefor the network traffic associated with the session(s)) to the detectionsystem if the following prefiltering analysis for HTTP traffic is a hit(i.e., satisfies both of the following example criteria based on headerlength and header encoding format, which is selected to reduce thevolume of traffic to be forwarded to cloud security 122 for furtheranalysis to detect potential Cobalt Strike Beacon C2 traffic, such aswill be further described below). First, the HTTP prefiltering moduledetermines whether the network traffic includes a header value or URIlength check that matches a range of 171 bytes to 256 bytes. Second, theHTTP prefiltering module determines whether the network traffic includesa header value or URI length field with encoding that matches one ofthese types of encoding: base64, base64url, netbios, netbiosu, or mask.Based on experiments (e.g., testing results), the prefiltering reducesthe amount of network traffic that is forwarded for further analysis bythe cloud security service to only approximately 0.32% of the totalnetwork traffic.

Cobalt Strike Beacon (CSB) detector 154 includes detection system 402,which includes an HTTP logic check module (e.g., sub-component, whichcan be implemented using Python or another high-level programminglanguage) that implements a decision tree based on the Source IP address(SrcIP), Destination IP address (DstIP), and Destination Port (DstPort)associated with each new session and performs the following checks basedon data statistics associated with each new session. As an initial logiccheck, detection system 402 can determine whether there is a priorverdict stored in a fast match table and if there is a match based onthe 3-tuple of SrcIP, DstIP, and DstPort, then the prior verdict isreturned to firewall 102 without further analysis/processing bydetection system 402.

Otherwise, processing proceeds to perform the following data statisticchecks that are stored in a data statistics table as shown at 414. Thefollowing data statistic checks are performed for each session andstored in the data statistics table for performing a behavior-baseddetection of Cobalt Strike Beacon C2 HTTP traffic (e.g., usingheuristic-based techniques as will now be further described). In anexample implementation, the fast match table and data statistics tablescan be implemented using an in-memory data structure store, such asusing an open source (e.g., Redis, which is publicly available athttps://redis.io/) or commercially available data store solution.

First, check the first twelve (12) sessions' timestamps to determinewhether such is a Gaussian or normal distribution. In an exampleimplementation, the Gaussian or normal distribution calculations can beimplemented using the Bowley Skewness algorithm for a normaldistribution calculation (e.g., the Bowley Skewness algorithm ispublicly available at https://www.statisticshowto.com/bowley-skewness/),and, specifically, to check whether it is a normal distribution for thetimestamps from the first twelve (12) sessions. The median absolutedeviation is determined using the median absolute deviation algorithm(e.g., the median absolute deviation algorithm is publicly available athttps://en.wikipedia.org/wiki/Median_absolute_deviation). Finally, theconnection count distribution is determined for the time of timestampdifference (TSdiff) for twelve (12) sessions from 6 seconds to 556seconds (e.g., 6/10-556/10, 1 second to 55 seconds per session, in thisexample implementation we choose 40 seconds for this connection countdistribution calculation).

Second, check whether a timestamp gap between different sessions is lessthan 10 minutes (e.g., Cobalt Strike Beacon C2 HTTP traffic generallysends heartbeat traffic every 5-10 minutes to check-in/communicate withits C2/Cobalt Strike Beacon Team server with metadata).

Third, check whether the HTTP header's MD5 hash value is the same (e.g.,meta data, including a cookie associated with the session(s), is thesame for each compromised machine, which results in the HTTP header'sMD5 being the same value for each session, new communication to its C2Team server).

Fourth, check whether the HTTP header field amount (i.e., number offields included in the HTTP header) is less than 10 fields in the HTTPheader (e.g., due to a design limitation resulting from Cobalt StrikeBeacon toolkit design implementation).

Fifth, check whether the HTTP header does not include a custom header.

Sixth, check whether the HTTP User-Agent (UA) is a popular UA (e.g., isa known/popular UA; an example listing of popular UAs is publiclyavailable athttps://www.whatismybrowser.com/guides/the-latest-user-agent/). CobaltStrike Beacon C2 traffic generally is associated with using aknown/popular UA as such HTTP network traffic attempts to simulatetypical user traffic that would be associated with such known/popularUAs (e.g., commonly used web browsers, such as Microsoft IE, Chrome,Mozilla, etc.).

Based on the above-described heuristics, if these logic checks result ina match, then it is determined that the network traffic for suchsessions is associated with Cobalt Strike Beacon C2 HTTP traffic. Ifthere is not a match, then it is determined that the network traffic forsuch sessions is not associated with Cobalt Strike Beacon C2 HTTPtraffic. The verdict is stored in the fast match table as shown at 416.Specifically, the 3-tuple (e.g., SrcIP, DstIP, Dstport) along with theverdict is added to the fast match table after the verdict is determinedas described above using the detection system. As such, the detectionsystem will query the fast match table for subsequent sessions assimilarly described above to facilitate a more efficient determinationof the verdict for previously analyzed HTTP traffic.

At 418, quality check system 404 performs a validation of results ofdetection system 402 to determine whether any prior verdicts were falsepositives or false negatives for Cobalt Strike Beacon C2 HTTP traffic.Specifically, a Destination (Dest) IP Probing and Validation module(e.g., sub-component) performs automated probing of the destination IPaddress by sending a custom HTTP request (e.g., custom HTTP/HTTPS/DNSrequest) to the destination IP address. As shown at 420, the Dest IPProbing and Validation module then determines whether a responseincludes a fingerprint associated with Cobalt Strike (e.g., HTTPresponse data includes a default certificate provided by Cobalt Strikeand/or the HTTP response matches a fingerprint for Cobalt Strike (CS);in an example implementation, the CS fingerprint is a predeterminedstring that is included in response traffic from the CS TeamServer, forexample when the client sends the HTTP request with a randomized URL tothe CS TeamServer, the TeamServer will respond with the HTTP status code404 and following example header Content-Type: text/plain\r\nDate: Wed,27 Feb. 2019 14:43:19 GMT\r\nContent-Length: 0, as such, we can use the“Content-Type: text/plain” and Content-Length: 0 as the fingerprint toidentify the CS TeamServer, such as further described below with respectto various embodiments) to validate a Cobalt Strike Beacon C2 HTTPtraffic (CS) verdict for network traffic/sessions in communication withthe destination IP address. As an example, the quality check system canperform a malware IP address lookup to determine whether the DestIP isassociated with Cobalt Strike Beacon related malware (e.g., a knownmalware sample that was previously identified as Cobalt Strike Beaconrelated malware based on prior malware analysis), and if so, the CobaltStrike Beacon C2 HTTP traffic (CS) verdict can be validated. Otherwise,the verdict is automatically changed from the CS verdict to a benignverdict (e.g., providing a feedback loop for improving CS detectionheuristics implemented in detection system 402). As such, the qualitycheck system can validate verdicts to attempt to detect any falsepositives or false negatives for Cobalt Strike Beacon C2 HTTP traffic.

The disclosed techniques for a behavior-based and cross-sessiondetection solution for performing Cobalt Strike Beacon C2 HTTP trafficdetection facilitate a 90% detection improvement rate for detection ofCobalt Strike Beacon C2 HTTP traffic based on experiments/testingresults as compared with preexisting IPS signature-based approaches(e.g., for default/known profiles for Cobalt Strike Beacon C2 HTTPtraffic).

Cobalt Strike Beacon HTTPS C2 Heuristic Detection

FIG. 4B illustrates portions of an example embodiment of a detectionsystem and a quality check system for processing of network traffic forperforming Cobalt Strike Beacon C2 HTTPS traffic detection in accordancewith some embodiments. As similarly discussed above with respect to FIG.4A, security platform 122 includes a Cobalt Strike Beacon (CSB) detector154. FIG. 4B similarly illustrates subcomponents of CSB detector 154including the following sub-components: a detection system 402 and aquality check system 404. However, as further described below, the HTTPSprefiltering logic module/sub-component and the HTTPS logic checkmodule/sub-component perform different prefiltering and logic checks forthe HTTPS traffic (e.g., as HTTPS traffic header/content is encrypted,distinct heuristics are disclosed herein for automatically detectingCobalt Strike beacon C2 HTTPS traffic as will now be further describedbelow).

Referring to FIG. 4B, a behavior-based and cross-session detectionsolution for performing Cobalt Strike beacon C2 HTTPS traffic detectionis performed using a cloud-based security service (e.g., cloud securityservice 122 as shown in FIG. 1 ) in coordination with a firewall 102.The behavior-based detection solution performs a cross-session check andincludes three components as will be described below.

Firewall 102 monitors network traffic on an enterprise network (e.g.,including HTTP traffic on enterprise network 140, such as shown in FIG.1 ). As shown at 410, firewall 102 monitors network traffic using a dataplane and performs a prefiltering analysis of the network traffic usingan HTTPS prefilter module (e.g., sub-component). As shown at 412,firewall 102 forwards the traffic (e.g., a packet capture (pcap) filefor the network traffic associated with the session(s)) to detectionsystem 402 if the following prefiltering analysis for HTTP traffic is ahit (i.e., satisfies both of the following example criteria based onServer hello random field value and includes “DOWNGRD” (e.g., this fieldvalue is present in Cobalt Strike beacon C2 HTTPS traffic but is alsoassociated with benign traffic so this prefiltering is used prior tofurther analysis that is performed using the below described detectionsystem that will check further heuristics), which is selected to reducethe volume of traffic to be forwarded to cloud security 122 for furtheranalysis to detect potential Cobalt Strike Beacon C2 traffic, such aswill be further described below). Specifically, the prefiltering moduledetermines whether the Server hello random field value includes a“DOWNGRD” value. Based on experiments (e.g., testing results), theprefiltering reduces the amount of network traffic that is forwarded forfurther analysis by the cloud security service and is only approximately0.002% of the total network traffic.

Cobalt Strike Beacon (CSB) detector 154 includes detection system 402,which includes an HTTPS logic check module (e.g., sub-component, whichcan be implemented using Python or another high-level programminglanguage) that implements a decision tree based on the Source IP address(SrcIP), Destination IP address (DstIP), and Destination Port (DstPort)associated with each new session and performs the following checks basedon data statistics associated with each new session. As an initial logiccheck, detection system 402 can determine whether there is a priorverdict stored in a fast match table and if there is a match based onthe 3-tuple of SrcIP, DstIP, and DstPort, then the prior verdict isreturned to firewall 102 without further analysis/processing bydetection system 402.

Otherwise, processing proceeds to perform the following data statisticchecks that are stored in a data statistics table as shown at 414. Thefollowing data statistic checks are performed for each session andstored in the data statistics table for performing a behavior-baseddetection of Cobalt Strike Beacon C2 HTTP traffic (e.g., usingheuristic-based techniques as will now be further described). In anexample implementation, the fast match table and data statistics tablescan be implemented using an in-memory data structure store, such asusing an open source (e.g., Redis, which is publicly available athttps://redis.io/) or commercially available data store solution.

First, check the first twelve (12) sessions' timestamps to determinewhether such is a Gaussian or normal distribution. In an exampleimplementation, the Gaussian or normal distribution calculations can beimplemented using the Bowley Skewness algorithm for a normaldistribution calculation (e.g., the Bowley Skewness algorithm ispublicly available at https://www.statisticshowto.com/bowley-skewness/),and, specifically, to check whether it is a normal distribution for thetimestamps from the first twelve (12) sessions. The Median absolutedeviation is determined using the Median absolute deviation algorithm(e.g., the Median absolute deviation algorithm is publicly available athttps://en.wikipedia.org/wiki/Median_absolute_deviation). Finally, theconnection count distribution is determined for the time of timestampdifference (TSdiff) for twelve (12) sessions from 6 seconds to 556seconds (e.g., 6/10-556/10, 1 second to 55 seconds per session, in thisexample implementation we choose 40 seconds for this connection countdistribution calculation).

Second, check the application data packet length from the HTTPS requestto determine whether it is the same or not. If the application datapacket length from the HTTPS request for each of these 12 sessions isthe same length, then this is another heuristic that is used as anotherindicator that this is likely associated with Cobalt Strike beacon C2HTTPS traffic.

Third, check whether there is only one application request data in therequest direction, and if there is, this is another heuristic that isused as another indicator that this is likely associated with CobaltStrike beacon C2 HTTPS traffic.

Based on the above-described heuristics, if these logic checks result ina match, then it is determined that the network traffic for suchsessions is associated with Cobalt Strike Beacon C2 HTTP traffic. Ifthere is not a match, then it is determined that the network traffic forsuch sessions is not associated with Cobalt Strike Beacon C2 HTTPtraffic. The verdict is stored in the fast match table as shown at 416.Specifically, the 3-tuple (e.g., SrcIP, DstIP, Dstport) along with theverdict is added to a fast match table after the verdict is determinedas described above using the detection system. As such, the detectionsystem will query the fast match table for subsequent sessions assimilarly described above to facilitate a more efficient determinationof the verdict for previously analyzed HTTP traffic.

At 418, quality check system 404 performs a validation of results ofdetection system 402 to determine whether any prior verdicts were falsepositives or false negatives for Cobalt Strike Beacon C2 HTTPS traffic.Specifically, a Destination (Dest) IP Probing and Validation module(e.g., sub-component) performs automated probing of the destination IPaddress by sending a custom HTTPS request (e.g., custom HTTP/HTTPS/DNSrequest) to the destination IP address. As shown at 420, the Dest IPProbing and Validation module then determines whether a responseincludes a fingerprint associated with Cobalt Strike (e.g., HTTPSresponse data includes a default certificate provided by Cobalt Strikeand/or the HTTPS response matches a fingerprint for Cobalt Strike (CS);in an example implementation, the CS fingerprint is a predeterminedstring that is be included in response traffic from the CS TeamServer,for example, when the client sends the HTTPS request with client helloto the CS TeamServer, the TeamServer will respond to the server helloand server certificate to the client, and by default the certificateincludes the CS keyword “Major Cobalt Strike”, as such, we can use thecertificate keyword “Major Cobalt Strike” as the fingerprint to identifythe CS TeamServer, such as further described below with respect tovarious embodiments) to validate a Cobalt Strike Beacon C2 HTTPS traffic(CS) verdict for network traffic/sessions in communication with thedestination IP address. As an example, the quality check system canperform a malware IP address lookup to determine whether the DestIP isassociated with Cobalt Strike Beacon related malware (e.g., a knownmalware sample that was previously identified as Cobalt Strike Beaconrelated malware based on prior malware analysis), and then the CobaltStrike Beacon C2 HTTPS traffic (CS) verdict can be validated. Otherwise,the verdict is automatically changed from the CS verdict to a benignverdict (e.g., providing a feedback loop for improving CS detectionheuristics implemented in detection system 402). As such, the qualitycheck system can validate verdicts to attempt to detect any falsepositives or false negatives for Cobalt Strike Beacon C2 HTTPS traffic.

The disclosed techniques for a behavior-based and cross-sessiondetection solution for performing Cobalt Strike Beacon C2 HTTPS trafficdetection facilitate a 90% detection improvement rate for detection ofCobalt Strike Beacon C2 HTTPS traffic based on experiments/testingresults as compared with preexisting IPS signature-based approaches(e.g., for default/known profiles for Cobalt Strike Beacon C2 HTTPStraffic).

Example Use Cases of Cobalt Strike Beacon HTTP C2 Heuristic Detection

FIG. 5A illustrates example attributes associated with Cobalt StrikeBeacon HTTP traffic that is used for heuristic detection in accordancewith some embodiments. Referring to FIG. 5A, this example networktraffic illustrates HTTP request headers MD hash that are the same asthe request URL as shown at 502 and the cookie (e.g., encoding base64)as shown at 504 that are associated with the sessions for such CobaltStrike Beacon HTTP traffic. As such, this example illustrates networktraffic that satisfies the prefiltering criteria as similarly describedabove with respect to FIG. 4A. First, the header value or URI lengthcheck matches a range of 171 bytes to 256 bytes. Second, the networktraffic includes a header value or URI length field with encoding ofbase64 and, thus, matches one of these types of encoding: base64,base64url, netbios, netbiosu, or mask. As a result, this network trafficas shown at 506 would be forwarded to cloud security 122 for furtheranalysis using CSB detector 154 as similarly described above withrespect to FIG. 4A.

FIG. 5B illustrates additional example attributes associated with CobaltStrike Beacon HTTP traffic that is used for heuristic detection inaccordance with some embodiments. Referring to FIG. 5B, this examplenetwork traffic illustrates HTTP request header fields that are fewerthan 10 request header fields as shown at 520 that is associated with aCobalt Strike (CS) HTTP request (i.e., only six (6) request headerfields are present in this example) as compared with a benign HTTPrequest at 522 that has a greater number of HTTP request header fields(i.e., greater than 10 request header fields) as shown at 522. As such,this example network traffic as shown at 520 satisfies theabove-described fourth heuristic related to the HTTP header field amount(i.e., number of fields included in the HTTP header is less than 10fields in the HTTP header) as similarly described above with respect toFIG. 4A.

FIG. 5C illustrates additional example attributes associated with CobaltStrike Beacon HTTP traffic that is used for heuristic detection inaccordance with some embodiments. Referring to FIG. 5C, this examplenetwork traffic illustrates HTTP user agent (UA) is a known UA and doesnot have a custom header as shown at 530. As such, this example networktraffic as shown at 530 satisfies the above-described sixth heuristicrelated to whether the HTTP User-Agent (UA) is a known/popular UA assimilarly described above with respect to FIG. 4A.

FIG. 5D illustrates additional example attributes associated with CobaltStrike Beacon HTTP traffic that is used for heuristic detection inaccordance with some embodiments. Referring to FIG. 5D, this examplenetwork traffic illustrates HTTP request header content that does nothave a custom header as shown at 540. As such, this example networktraffic as shown at 540 satisfies the above-described fifth heuristicrelated to whether the HTTP header does not include a custom header assimilarly described above with respect to FIG. 4A.

Example Use Cases of Cobalt Strike Beacon HTTPS C2 Heuristic Detection

FIG. 5E illustrates additional example attributes associated with CobaltStrike Beacon HTTPS traffic that is used for heuristic detection inaccordance with some embodiments. Referring to FIG. 5E, as shown at 550,this example network traffic illustrates an HTTPS request in which theServer hello random field value includes a “DOWNGRD” value as similarlydescribed above with respect to FIG. 4B. As a result, this networktraffic as shown at 506 would be forwarded to cloud security 122 forfurther analysis using CSB detector 154 as similarly described abovewith respect to FIG. 4B.

FIG. 5F illustrates additional example attributes associated with CobaltStrike Beacon HTTPS traffic that is used for heuristic detection inaccordance with some embodiments. Referring to FIG. 5F, as shown at 560,this example network traffic illustrates the SSL application data amountin the request direction as similarly described above with respect toFIG. 4B. Specifically, as similarly described above with respect to FIG.4B, the second check performed by the HTTPS logic module is to determinewhether the application data packet length from the HTTPS request is thesame or not. If the application data packet length from the HTTPSrequest for each of these 12 sessions is the same length as shown at 560(e.g., 1365 bytes), then this is another heuristic that is used asanother indicator that this is likely associated with Cobalt Strikebeacon C2 HTTPS traffic.

FIG. 5G illustrates additional example attributes associated with CobaltStrike Beacon HTTPS traffic that is used for heuristic detection inaccordance with some embodiments. Referring to FIG. 5G, as shown at 570,this example network traffic illustrates the SSL application data amountin the request direction as similarly described above with respect toFIG. 4B. Specifically, as similarly described above with respect to FIG.4B, the third check performed by the HTTPS logic module is to determinewhether there is only one application request data (e.g., and length is459 bytes) in the request direction (e.g., heuristics performed for eachof these analyzed (12) sessions), which is another heuristic that isused as another indicator that this is likely associated with CobaltStrike beacon C2 HTTPS traffic.

Additional example processes for the disclosed techniques for CobaltStrike Beacon HTTP/HTTPS C2 heuristic detection will now be described.

Example Processes for Cobalt Strike Beacon HTTP C2 Heuristic Detection

FIG. 6 is a flow diagram of a process for Cobalt Strike Beacon HTTP C2heuristic detection in accordance with some embodiments. In someembodiments, a process 600 as shown in FIG. 6 is performed by thesecurity platform and techniques as similarly described above includingthe embodiments described above with respect to FIGS. 1-5G. In oneembodiment, process 600 is performed by data appliance 102 as describedabove with respect to FIG. 1 , security platform 122 as described abovewith respect to FIG. 1 (e.g., as a cloud-based security service), avirtual appliance (e.g., Palo Alto Networks' VM Series virtualized nextgeneration firewalls, CN Series container next generation firewalls,and/or other commercially available virtual-based or container-basedfirewalls can similarly be implemented and configured to perform thedisclosed techniques), an SDN security solution, a cloud securityservice, and/or combinations or hybrid implementations of theaforementioned as described herein.

At 602, HTTP network traffic is monitored at a firewall. For example,the firewall can utilize an application identification to detect HTTPtraffic, such as similarly described above with respect to FIGS. 1 and4A.

At 604, prefiltering of the monitored HTTP network traffic is performedat the firewall to select a subset of the HTTP network traffic toforward to a cloud security service. For example, an HTTP prefilteringmodule can perform heuristic analysis of the HTTP network traffic toselect a subset of HTTP traffic sessions to forward to the cloudsecurity service for further analysis, such as similarly described abovewith respect to FIGS. 4A and 5A-5D.

At 606, determining whether the subset of the HTTP network traffic isassociated with Cobalt Strike Beacon HTTP C2 traffic activity based on aplurality of heuristics is performed. For example, an HTTP logic checkmodule can perform further heuristic analysis of the HTTP networktraffic to automatically detect Cobalt Strike Beacon HTTP C2 traffic,such as similarly described above with respect to FIGS. 4A and 5A-5D.

At 608, an action is performed in response to detecting Cobalt StrikeBeacon HTTP C2 traffic activity. The security platform (122) and/or dataappliance (102) can then perform an action based on a policy (e.g.,security/C2 related malware policy, which can be stored in policies 252as shown in FIG. 2B) in response to the malware verdict. For example,the data appliance can be configured to block the Cobalt Strike BeaconHTTP C2 traffic activity. Other example actions can include blockingaccess to the destination IP address associated with the detected CobaltStrike Beacon HTTP C2 traffic activity, blocking/dropping the networktraffic associated with the detected Cobalt Strike Beacon HTTP C2traffic activity and/or associated with that destination IP address,alerting an endpoint user and/or a network/security administrator thatthe endpoint was associated with the detected Cobalt Strike Beacon HTTPC2 traffic activity, quarantining an endpoint device associated with thedetected Cobalt Strike Beacon HTTP C2 traffic activity, identifying thedestination IP address, URL, etc. associated with the detected CobaltStrike Beacon HTTP C2 traffic activity as malicious (or potentiallymalicious), and/or various other actions can also be performed based onthe policy.

Example Processes for Cobalt Strike Beacon HTTPS C2 Heuristic Detection

FIG. 7 is a flow diagram of a process for Cobalt Strike Beacon HTTPS C2heuristic detection in accordance with some embodiments. In someembodiments, a process 700 as shown in FIG. 7 is performed by thesecurity platform and techniques as similarly described above includingthe embodiments described above with respect to FIGS. 1-5G. In oneembodiment, process 700 is performed by data appliance 102 as describedabove with respect to FIG. 1 , security platform 122 as described abovewith respect to FIG. 1 (e.g., as a cloud-based security service), avirtual appliance (e.g., Palo Alto Networks' VM Series virtualized nextgeneration firewalls, CN Series container next generation firewalls,and/or other commercially available virtual-based or container-basedfirewalls can similarly be implemented and configured to perform thedisclosed techniques), an SDN security solution, a cloud securityservice, and/or combinations or hybrid implementations of theaforementioned as described herein.

At 702, HTTPS network traffic is monitored at a firewall. For example,the firewall can utilize an application identification to detect HTTPStraffic, such as similarly described above with respect to FIGS. 1 and4B.

At 704, prefiltering of the monitored HTTPS network traffic is performedat the firewall to select a subset of the HTTPS network traffic toforward to a cloud security service. For example, an HTTPS prefilteringmodule can perform heuristic analysis of the HTTPS network traffic toselect a subset of HTTPS traffic sessions to forward to the cloudsecurity service for further analysis, such as similarly described abovewith respect to FIGS. 4B and 5E-5G.

At 706, determining whether the subset of the HTTPS network traffic isassociated with Cobalt Strike Beacon HTTPS C2 traffic activity based ona plurality of heuristics is performed. For example, an HTTPS logiccheck module can perform further heuristic analysis of the HTTPS networktraffic to automatically detect Cobalt Strike Beacon HTTPS C2 traffic,such as similarly described above with respect to FIGS. 4B and 5E-5G.

At 708, an action is performed in response to detecting Cobalt StrikeBeacon HTTPS C2 traffic activity. The security platform (122) and/ordata appliance (102) can then perform an action based on a policy (e.g.,security/C2 related malware policy, which can be stored in policies 252as shown in FIG. 2B) in response to the malware verdict. For example,the data appliance can be configured to block the Cobalt Strike BeaconHTTPS C2 traffic activity. Other example actions can include blockingaccess to the destination IP address associated with the detected CobaltStrike Beacon HTTPS C2 traffic activity, blocking/dropping the networktraffic associated with the detected Cobalt Strike Beacon HTTPS C2traffic activity and/or associated with that destination IP address,alerting an endpoint user and/or a network/security administrator thatthe endpoint was associated with the detected Cobalt Strike Beacon HTTPSC2 traffic activity, quarantining an endpoint device associated with thedetected Cobalt Strike Beacon HTTPS C2 traffic activity, identifying thedestination IP address, URL, etc. associated with the detected CobaltStrike Beacon HTTPS C2 traffic activity as malicious (or potentiallymalicious), and/or various other actions can also be performed based onthe policy.

Probing for Cobalt Strike Teamserver Detection

Another technical challenge is to verify that a target IP address (e.g.,extracted based on the above-described techniques) is hosting a CobaltStrike TeamServer.

Accordingly, various techniques for probing (e.g., active probing) forCobalt Strike TeamServer detection are disclosed. For example, thedisclosed techniques utilize active probing of an IP address to detectwhether the IP address is hosting a Cobalt Strike TeamServer based onresponses to the active probing (e.g., packets sent in response from thetarget IP address to probe packets).

As similarly described above with respect to FIGS. 4A and 4B, adestination IP probing and validation component/module of quality checksystem 404 performs active probing (e.g., over HTTP or HTTPS) of an IPaddress to detect whether the IP address is hosting a Cobalt StrikeTeamServer based on responses (e.g., packets sent in response from thetarget IP address to probe packets) to the active probing. As will befurther described below, the disclosed active probing techniques forCobalt Strike TeamServer detection can be performed using variousnetwork protocols, including, for example, HTTP, HTTPS, and/or DNS.

Generally, the destination IP probing and validation module extract thedestination (dst) IP address (dst IP) and port number (port) informationfrom the network traffic that was forwarded/collected from a firewallthat monitors network traffic, as similarly described above with respectto FIGS. 4A and 4B. Next, the destination IP probing and validationmodule sends a crafted request (e.g., a crafted HTTP/HTTPS/DNS request)to the dst IP and port (e.g., the target). The destination IP probingand validation module then checks/evaluates the response(s) receivedfrom the target. If the response content matches with a predeterminedfingerprint and detection logic, then the dst IP, port, and a maliciousverdict is inserted into the fast match table (e.g., the fast matchtable of detection system 402 as similarly described above with respectto FIGS. 4A and 4B). Otherwise (i.e., there was not a match with thepredetermined fingerprint and detection logic), then the dst IP, port,and a benign verdict is inserted into the fast match table.

HTTP/HTTPS Probingfor Cobalt Strike TeamServer Detection

An example implementation for HTTP/HTTPS active probing for CobaltStrike TeamServer detection will now be described. As an initialprecheck operation of the probing logic, the destination IP probing andvalidation module sends a crafted precheck HTTP/HTTPS request to the dstIP and port (e.g., the target). The example crafted precheck HTTP/HTTPSrequest is as follows: http[s]:H[IP]:[Port]/index.html. The destinationIP probing and validation module then checks/evaluates the response tothe crafted precheck HTTP/HTTPS request. Specifically, if the followingis received in the response from the target, then it is deemedsuspicious (e.g., may (likely) be a Cobalt Strike TeamServer) andfurther active probing and evaluation will be performed. Morespecifically, if the response includes a status code equals 404 and theHTTP/HTTPS header includes the following fields and values: ‘Date’ anddate value (e.g., ‘Wed, 9 Feb. 2022 23:09:48 GMT’); ‘Content-Type’:‘text/plain’; and ‘Content-Length’: ‘0’. Otherwise (e.g., these fieldsand values are not present in the response from the target), then it isdetermined that the target is not a Cobalt Strike TeamServer and furtheractive probing and evaluation will not be performed and the verdict willbe determined to be benign (and the fast match table can be updated assimilarly described above with respect to FIGS. 4A and 4B).

As a final precheck operation of the HTTP/HTTPS probing logic, thedestination IP probing and validation module sends a crafted HTTP/HTTPSrequest to the target to download the beacon file. The example craftedfinal precheck HTTP/HTTPS request includes a URL value of “Swb1” and isas follows: http[s]://[IP]:[Port]/Swb1. In this example, “Swb1” can beany 4 bytes that satisfies the following checksum requirements: 4-bytesURL checksum value equals 92 or 93 based on the platform, such as usingthe checksum algorithm as shown in FIG. 8 in which the input string textis the 4-bytes URL value. FIG. 8 illustrates a checksum algorithm forprobing logic for HTTP/HTTPS Cobalt Strike TeamServer detection inaccordance with some embodiments. The destination IP probing andvalidation module then checks/evaluates the response to the craftedfinal check HTTP/HTTPS request. If the target is a Cobalt StrikeTeamServer, then the response from the target will return a status codeequals 200, the HTTP/HTTPS header ‘Content-Length’ value is greater than200 k and less than 300 k, and the HTTP/HTTPS body first 2 bytes equals0xFC48 or 0xFCe8 (e.g., as such is present in the Cobalt Strike sharedcode for the beacon file based on heuristics analysis of Cobalt StrikeTeamServer behavior and the content of its beacon file). Otherwise(e.g., the response does not match the above-described criteria), and asa result, it is determined that the target is not a Cobalt StrikeTeamServer and further active probing and evaluation will not beperformed and the verdict will be determined to be benign (and the fastmatch table can be updated as similarly described above with respect toFIGS. 4A and 4B).

DNS Probingfor Cobalt Strike TeamServer Detection

An example implementation for DNS active probing for Cobalt StrikeTeamServer detection will now be described. As an initial precheckoperation of the probing logic, the destination IP probing andvalidation module performs a final check for the DNS probing of the dstIP and port (e.g., the target). As a final precheck operation of the DNSprobing logic, the destination IP probing and validation module sends acrafted DNS request to the target to download the beacon file. FIG. 9Aillustrates an example DNS request for performing active probing of atarget in accordance with some embodiments. Specifically, a crafted DNSrequest is sent to the target to download the beacon file. Morespecifically, the crafted DNS request is a txt DNS request with domainaaa.stage.xxxx as shown in FIG. 9A.

The destination IP probing and validation module then checks the contentof the DNS response from the target. If the target is a Cobalt StrikeTeamServer, then the response from the target will return a txt DNSrecord response with content that begins with “WYIIIIIIIIIIII” (e.g., assuch is present in the Cobalt Strike base64 encoding for the shared codefor the beacon file based on heuristics analysis of Cobalt StrikeTeamServer behavior and the content of its beacon file), such as shownin FIG. 9B. FIG. 9B illustrates an example DNS response to activeprobing of a target in accordance with some embodiments. Otherwise(e.g., the response does not match the above-described criteria), and asa result, it is determined that the target is not a Cobalt StrikeTeamServer and further active probing and evaluation will not beperformed and the verdict will be determined to be benign (and the fastmatch table can be updated as similarly described above with respect toFIGS. 4A and 4B).

Additional example processes for the disclosed techniques for probingfor Cobalt Strike TeamServer detection will now be described.

Example Processes for HTTP/HTTPS Probing for Cobalt Strike TeamServerDetection

FIG. 10 is a flow diagram of a process for HTTP/HTTPS probing for CobaltStrike TeamServer detection in accordance with some embodiments. In someembodiments, a process 1000 as shown in FIG. 10 is performed by thesecurity platform and techniques as similarly described above includingthe embodiments described above with respect to FIGS. 1-8 . In oneembodiment, process 1000 is performed by data appliance 102 as describedabove with respect to FIG. 1 , security platform 122 as described abovewith respect to FIG. 1 (e.g., as a cloud-based security service), avirtual appliance (e.g., Palo Alto Networks' VM Series virtualized nextgeneration firewalls, CN Series container next generation firewalls,and/or other commercially available virtual-based or container-basedfirewalls can similarly be implemented and configured to perform thedisclosed techniques), an SDN security solution, a cloud securityservice, and/or combinations or hybrid implementations of theaforementioned as described herein.

At 1002, HTTP/HTTPS network traffic is monitored at a firewall. Forexample, the firewall can utilize an application identification todetect HTTP/HTTPS traffic, such as similarly described above withrespect to FIGS. 1 and 4A-4B.

At 1004, prefiltering of the monitored HTTP/HTTPS network traffic isperformed at the firewall to select a subset of the HTTP/HTTPS networktraffic to forward to a cloud security service. For example, anHTTP/HTTPS prefiltering module can perform heuristic analysis of theHTTP/HTTPS network traffic to select a subset of HTTP/HTTPS trafficsessions to forward to the cloud security service for further analysis,such as similarly described above with respect to FIGS. 4A-4B and 5A-5G.

At 1006, HTTP/HTTPS probing of a target to detect whether the target isa Cobalt Strike TeamServer is performed. For example, a destination IPprobing and validation module can perform further heuristic analysis ofthe responses from the target to automatically detect a Cobalt StrikeTeamServer, such as similarly described above with respect to FIGS.4A-4B, 5A-5G and 8 .

At 1008, an action is performed in response to detecting that the targetis the Cobalt Strike TeamServer. The security platform (122) and/or dataappliance (102) can then perform an action based on a policy (e.g.,security/C2 related malware policy, which can be stored in policies 252as shown in FIG. 2B) in response to the malware verdict. For example,the data appliance can be configured to block the Cobalt Strike BeaconHTTP/HTTPS C2 traffic activity. Other example actions can includeblocking access to the destination IP address associated with thedetected Cobalt Strike Beacon HTTP/HTTPS C2 traffic activity,blocking/dropping the network traffic associated with the detectedCobalt Strike Beacon HTTP/HTTPS C2 traffic activity and/or associatedwith that destination IP address, alerting an endpoint user and/or anetwork/security administrator that the endpoint was associated with thedetected Cobalt Strike Beacon HTTP/HTTPS C2 traffic activity,quarantining an endpoint device associated with the detected CobaltStrike Beacon HTTP/HTTPS C2 traffic activity, identifying thedestination IP address, URL, etc. associated with the detected CobaltStrike Beacon HTTP/HTTPS C2 traffic activity as malicious (orpotentially malicious), and/or various other actions can also beperformed based on the policy.

Example Processes for DNS Probing for Cobalt Strike TeamServer Detection

FIG. 11 is a flow diagram of a process for DNS probing for Cobalt StrikeTeamServer detection in accordance with some embodiments. In someembodiments, a process 1100 as shown in FIG. 11 is performed by thesecurity platform and techniques as similarly described above includingthe embodiments described above with respect to FIGS. 1-7 and 9A-B. Inone embodiment, process 1100 is performed by data appliance 102 asdescribed above with respect to FIG. 1 , security platform 122 asdescribed above with respect to FIG. 1 (e.g., as a cloud-based securityservice), a virtual appliance (e.g., Palo Alto Networks' VM Seriesvirtualized next generation firewalls, CN Series container nextgeneration firewalls, and/or other commercially available virtual-basedor container-based firewalls can similarly be implemented and configuredto perform the disclosed techniques), an SDN security solution, a cloudsecurity service, and/or combinations or hybrid implementations of theaforementioned as described herein.

At 1102, DNS network traffic is monitored at a firewall. For example,the firewall can utilize an application identification to detect DNStraffic, such as similarly described above with respect to FIGS. 1 and4A-4B.

At 1104, prefiltering of the monitored DNS network traffic is performedat the firewall to select a subset of the DNS network traffic to forwardto a cloud security service. For example, an DNS prefiltering module canperform heuristic analysis of the DNS network traffic to select a subsetof DNS traffic sessions to forward to the cloud security service forfurther analysis, such as similarly described above with respect toFIGS. 4A-4B and 5A-5G and further described below.

In an example implementation of a DNS prefiltering operation such assimilarly shown at 410 in FIG. 4A can be performed by a DNS prefiltermodule implemented on firewall 102 by performing the following heuristicanalysis: if the DNS request is a DNS txt record (e.g.,dns.qry.type==16(txt record)), then the traffic is forwarded to thecloud for further Cobalt Strike detection analysis.

At 1106, DNS probing of a target to detect whether the target is aCobalt Strike TeamServer is performed. For example, a destination IPprobing and validation module can perform further heuristic analysis ofthe responses from the target to automatically detect a Cobalt StrikeTeamServer, such as similarly described above with respect to FIGS.4A-4B, 5A-5G, and 9A-9B.

At 1108, an action is performed in response to detecting that the targetis the Cobalt Strike TeamServer. The security platform (122) and/or dataappliance (102) can then perform an action based on a policy (e.g.,security/C2 related malware policy, which can be stored in policies 252as shown in FIG. 2B) in response to the malware verdict. For example,the data appliance can be configured to block the Cobalt Strike BeaconDNS C2 traffic activity. Other example actions can include blockingaccess to the destination IP address associated with the detected CobaltStrike Beacon DNS C2 traffic activity, blocking/dropping the networktraffic associated with the detected Cobalt Strike Beacon DNS C2 trafficactivity and/or associated with that destination IP address, alerting anendpoint user and/or a network/security administrator that the endpointwas associated with the detected Cobalt Strike Beacon DNS C2 trafficactivity, quarantining an endpoint device associated with the detectedCobalt Strike Beacon DNS C2 traffic activity, identifying thedestination IP address, URL, etc. associated with the detected CobaltStrike Beacon DNS C2 traffic activity as malicious (or potentiallymalicious), and/or various other actions can also be performed based onthe policy.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A system, comprising: a processor configured to:monitor HyperText Transfer Protocol Secure (HTTPS) network traffic at afirewall; prefilter the monitored HTTPS network traffic at the firewallto select a subset of the HTTPS network traffic to forward to a cloudsecurity service; determine whether the subset of the HTTPS networktraffic is associated with Cobalt Strike Beacon HTTPS C2 trafficactivity based on a plurality of heuristics; and perform an action inresponse to detecting the Cobalt Strike Beacon HTTPS C2 trafficactivity; and a memory coupled to the processor and configured toprovide the processor with instructions.
 2. The system of claim 1,wherein a fast match table of a detection system stores previouslydetected Cobalt Strike Beacon HTTPS C2 traffic activity.
 3. The systemof claim 1, wherein a fast match table of a detection system stores a3-tuple of previously detected Cobalt Strike Beacon HTTPS C2 trafficactivity, wherein the 3-tuple includes a source IP address, adestination IP address, and a destination port.
 4. The system of claim1, wherein data statistics based on an automated heuristic analysis ofthe subset of the HTTPS network traffic is stored in a data statisticstable of a detection system.
 5. The system of claim 1, wherein theprocessor is further configured to perform a validation of the detectedCobalt Strike Beacon HTTPS C2 traffic activity.
 6. The system of claim1, wherein the processor is further configured to perform a validationof the detected Cobalt Strike Beacon HTTPS C2 traffic activity based onprobing of a destination IP address associated with the detected CobaltStrike Beacon HTTPS C2 traffic activity.
 7. The system of claim 1,wherein the processor is further configured to perform a validation ofthe detected Cobalt Strike Beacon HTTPS C2 traffic activity based onprobing of a destination IP address associated with the detected CobaltStrike Beacon HTTPS C2 traffic activity and using a fingerprint datastore.
 8. A method, comprising: monitoring HyperText Transfer ProtocolSecure (HTTPS) network traffic at a firewall; prefiltering the monitoredHTTPS network traffic at the firewall to select a subset of the HTTPSnetwork traffic to forward to a cloud security service; determiningwhether the subset of the HTTPS network traffic is associated withCobalt Strike Beacon HTTPS C2 traffic activity based on a plurality ofheuristics; and performing an action in response to detecting the CobaltStrike Beacon HTTPS C2 traffic activity.
 9. The method of claim 8,wherein a fast match table of a detection system stores previouslydetected Cobalt Strike Beacon HTTPS C2 traffic activity.
 10. The methodof claim 8, wherein a fast match table of a detection system stores a3-tuple of previously detected Cobalt Strike Beacon HTTPS C2 trafficactivity, wherein the 3-tuple includes a source IP address, adestination IP address, and a destination port.
 11. The method of claim8, wherein data statistics based on an automated heuristic analysis ofis the subset of the HTTPS network traffic is stored in a datastatistics table of a detection system.
 12. The method of claim 8,further comprising performing a validation of the detected Cobalt StrikeBeacon HTTPS C2 traffic activity.
 13. The method of claim 8, furthercomprising performing a validation of the detected Cobalt Strike BeaconHTTPS C2 traffic activity based on probing of a destination TP addressassociated with the detected Cobalt Strike Beacon HTTPS C2 trafficactivity.
 14. The method of claim 8, further comprising performing avalidation of the detected Cobalt Strike Beacon HTTPS C2 trafficactivity based on probing of a destination TP address associated withthe detected Cobalt Strike Beacon HTTPS C2 traffic activity and using afingerprint data store.
 15. A computer program product embodied in anon-transitory computer readable medium and comprising computerinstructions for: monitoring HyperText Transfer Protocol Secure (HTTPS)network traffic at a firewall; prefiltering the monitored HTTPS networktraffic at the firewall to select a subset of the HTTPS network trafficto forward to a cloud security service; determining whether the subsetof the HTTPS network traffic is associated with Cobalt Strike BeaconHTTPS C2 traffic activity based on a plurality of heuristics; andperforming an action in response to detecting the Cobalt Strike BeaconHTTPS C2 traffic activity.
 16. The computer program product of claim 15,wherein a fast match table of a detection system stores previouslydetected Cobalt Strike Beacon HTTPS C2 traffic activity.
 17. Thecomputer program product of claim 15, wherein a fast match table of adetection system stores a 3-tuple of previously detected Cobalt StrikeBeacon HTTPS C2 traffic activity, wherein the 3-tuple includes a sourceIP address, a destination IP address, and a destination port.
 18. Thecomputer program product of claim 15, further comprising computerinstructions for performing a validation of the detected Cobalt StrikeBeacon HTTPS C2 traffic activity.
 19. The computer program product ofclaim 15, further comprising computer instructions for performing avalidation of the detected Cobalt Strike Beacon HTTPS C2 trafficactivity based on probing of a destination IP address associated withthe detected Cobalt Strike Beacon HTTPS C2 is traffic activity.
 20. Thecomputer program product of claim 15, further comprising computerinstructions for performing a validation of the detected Cobalt StrikeBeacon HTTPS C2 traffic activity based on probing of a destination IPaddress associated with the detected Cobalt Strike Beacon HTTPS C2traffic activity and using a fingerprint data store.